University of Tasmania
Browse

File(s) under permanent embargo

Assessing the stability of canopy maps produced from UAV-LiDAR data

conference contribution
posted on 2023-05-23, 08:31 authored by Luke WallaceLuke Wallace
The use of Unmanned Aerial Vehicles (UAVs) as a remote sensing platform offers a unique combination of high resolution data collected within relatively low cost targeted missions. This paper investigates the use of UAV-borne Light Detecting and Ranging (LIDAR) systems (UAVL) as a platform to gain knowledge of the canopy structure within forested environments. Repeat datasets were collected with a UAVL system over six Eucalyptus Globulus plots with varying levels of canopy cover. The relative stability of four metrics for estimating canopy structure, First Cover Index (FCI), Last Cover Index (LCI), a Grid based method (GCI) and an Alpha shape based method (ACI) were assessed using these repeat datasets. It is shown that the repeatability of the GCI metric is subject to variations in plot level point density (standard deviation of 4.06 %). Instabilities in the FCI (1.91 %) and LCI (2.28 %) metrics were found to be related to the properties of the sensor and the lasers interaction with the canopy. The ACI metric (1.86 %) was found to be the most stable.

History

Publication title

Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Editors

C Fraser, J Walker and M Williams

Pagination

3879-3882

ISBN

978-1-4799-1114-1

Department/School

School of Geography, Planning and Spatial Sciences

Publisher

IEEE

Place of publication

USA

Event title

2013 IEEE International Geoscience and Remote Sens

Event Venue

Melbourne, Australia

Date of Event (Start Date)

2013-07-21

Date of Event (End Date)

2013-07-26

Rights statement

Copyright 2013 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the environmental sciences

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC